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An Investigation of the Mesoscale Predictability over the Northeast U.S. Brian A. Colle, Matthew Jones, and Joseph Olson Institute for Terrestrial and Planetary Sciences Stony Brook University / SUNY and Jeffrey S. Tongue NOAA/NWS Upton, NY.
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An Investigation of the Mesoscale Predictability over the Northeast U.S. Brian A. Colle, Matthew Jones, and Joseph Olson Institute for Terrestrial and Planetary Sciences Stony Brook University / SUNY and Jeffrey S. Tongue NOAA/NWS Upton, NY
Deterministic Runs since 1999 using Eta init/bdy: 00/12Z (60 h – 36/12 km domains, 12-36 h 4-km nest). Grell (36/12 km), Simple ice, MRF PBL.
SNE Sea breeze verification (~ 40 events): MM5 wind direction mean absolute errors (36 – 12 km) Problem: Sea breezes move inland too fast (At ISP, 50% early, 38% on time, and 16% late)
Data sampling over Long Island Sound using the Port Jefferson to Bridgeport Ferry: http://www.stonybrook.edu/soundscience/
360 0 17:00 GMT June 23rd, 2003 N Bridgeport 18:10 GMT arrival = 1 m/s Model Winds Observed Winds Port Jefferson 17 GMT departure
Warm season cool bias over water Port Jefferson Bridgeport Cool season warm bias over water
MM5 storm total precipitation (a) 36-km (c) 1.33 km (b) 12-km
18-Member MM5 Ensemble (00 UTC cycle, 36/12 km domains) INITIAL CONDITION ENSEMBLE MATRIX(each uses Grell-MRF physical combination) PHYSICS ENSEMBLE MATRIX
Nor-easter 6 DEC 2003 L 21Z 6 DEC 12-km Refl, 700 mb fronto (21 h) 0 oC isotherm (21 h) 1 = .01- 0.1 in 2 = .1 - .25 in Most Prob cat pcp 21-24 h
2-m Temperature Mean Errors (12 km domain, warm season 2003) day Night Day Night Day
2-m Temperature Mean Absolute Errors (12-km domain) Night Day Night Day
2-m Temperature Mean Absolute Errors (Islip, NY) Night Day Night Day
2-m Temperature Mean Absolute Errors (12-km domain) Night Day Night Day
Summary and Future Work • Stony Brook University has been running the MM5 in real-time and completing verification during the past 4 years. We are currently testing the WRF model. • Mesoscale predictions of the sea breeze are hindered by parameterization problems (excessive surface fluxes) as suggested by cross-Sound ferry data and standard verification. For Floyd (1999), there were large precipitation sensitivities with the convective and PBL physics. • An 18-member ensemble forecast system down to 12-km grid spacing has been developed to better quantify uncertainty over the Northeast at moderately high resolution. • The physics members were more useful than the IC members for temperature and windspeed due to large model biases. 14-day bias correction and MOS can provide additional significant improvements. • Warm season precipitation will soon be verified from the 18 members.
REAL-TIME WEB PAGE http://atmos.msrc.sunysb.edu/mm5rt REAL-TIME REFERENCES (2003) Colle, B. A., Olson, J. B., Tongue, J. S. 2003: Multiseason Verification of the MM5. Part I: Comparison with the Eta Model over the Central and Eastern United States and Impact of MM5 Resolution.Weather and Forecasting, 18, 431–457. Colle, B. A., Olson, J. B., Tongue, J. S. 2003: Multiseason Verification of the MM5. Part II: Evaluation of High-Resolution Precipitation Forecasts over the Northeastern United States.Weather and Forecasting: 18, 458–480. Colle, B. A. 2003: Numerical Simulations of the Extratropical Transition of Floyd (1999): Responsible Mechanisms for the Heavy Precipitation.Mon. Wea. Rev., 131, 2905=2926. Roebber, P, D. Schultz, B. Colle, and D. Stensrud, 2003: The Risks and Rewards of High Resolution and Ensemble Forecasting. Submitted to Weather and Forecasting.
Summary and Future Plans • Stony Brook University has been running the MM5 in real-time and completing verification for the past 4 years. We are currently testing parallel runs of WRF in real-time. • Develop case studies to demonstrate how to use ensemble and high resolution data in the forecast process. • Add more ensemble members (NOGAPS and CMC grids, warm start runs using observations). • Use ensembles in various applications (energy load, wave modeling, storm surge, etc…) • Add more data sets for verification (e.g., ferry data, scatterometer) and test new verification approaches.